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Hot forging is one of the common manufacturing processes for producing brass workpieces. However forging produces flash which is a thin metal part around the desired part formed with an excessive material. Using robots with vision system to manipulate this workpiece has encountered several challenging issues, e.g. the uncertain shape of flash, color, reflection of brass surface, different lighting condition, and the uncertainty surrounding the position and orientation of the workpiece. In this research, Mask region-based convolutional neural network together with image processing is used to resolve these issues. The depth camera can provide images for visual detection. Machine learning Mask region-based convolutional neural network model was trained with color images and the position of the object is determined by the depth image. A dual arm 7 degree of freedom collaborative robot with proposed grasping strategy is used to grasp the workpiece that can be in inappropriate position and pose. Eventually, experiments were conducted to assess the visual detection process and the grasp planning of the robot. [ABSTRACT FROM AUTHOR]

With the modernization of traditional Chinese medicine (TCM), creating devices to digitalize aspects of pulse diagnosis has proved to be challenging. The currently available pulse detection devices usually rely on external pressure devices, which are either bulky or poorly integrated, hindering their practical application. In this work, we propose an innovative wearable active pressure three-channel pulse monitoring device based on TCM pulse diagnosis methods. It combines a flexible pressure sensor array, flexible airbag array, active pressure control unit, advanced machine learning approach, and a companion mobile application for human–computer interaction. Due to the high sensitivity (460.1 kPa−1), high linearity (R2 > 0.999) and flexibility of the flexible pressure sensors, the device can accurately simulate finger pressure to collect pulse waves (Cun, Guan, and Chi) at different external pressures on the wrist. In addition, by measuring the change in pulse wave amplitude at different pressures, an individual’s blood pressure status can be successfully predicted. This enables truly wearable, actively pressurized, continuous wireless dynamic monitoring of wrist pulse health. The innovative and integrated design of this pulse monitoring platform could provide a new paradigm for digitizing aspects of TCM and other smart healthcare systems. [ABSTRACT FROM AUTHOR]

The primary goal of a microgrid (MG) operator is to provide electricity to consumers while minimizing costs. For this aim, the operator must engage in the cost-effective management of its resource outputs, which can encompass electrical, thermal, or combined cooling, heat and power (CCHP) systems. Conversely, there has been a growing emphasis on enhancing the resilience of MGs in response to low-probability high-impact (LPHI) incidents in recent years. Therefore, MG-associated energy management strategies have to factor in resilience considerations. While resilience improvement activities increase the operational cost, they lead to a reduction in lost load, and subsequently, a decrease in the MG outage costs, making these activities economically viable. This paper focuses on MGs' energy management with the primary goals of enhancing resilience, minimizing operational costs, and mitigating active power losses as well as environmental pollution. To attain this goal, various means like renewable resources (specifically photovoltaic (PV) and wind turbine (WT) systems), CCHP, and energy storage devices are integrated. Additionally, for reaching the solution, a genetic algorithm (GA) is implemented. MG operation considers the resilience concept, and according to the obtained results, it is observed that the cost of operation and environmental pollution, respectively, experience an increase about 6.31% and 2.8%. However, due to the reduction in outage costs by an average of 13.91% and power losses by 0.5%, the overall cost is diminished about 5.93%. This cost reduction is achieved through increased CCHP generation and a decreased outage duration during emergencies. [ABSTRACT FROM AUTHOR]

This paper presents a multi-layer, multi-objective (MLMO) optimization model for techno-economic-environmental energy management in cooperative multi-Microgrids (MMGs) that incorporates a Demand Response Program (DRP). The proposed MLMO approach simultaneously optimizes operating costs, MMG operator benefits, environmental emissions, and MMG dependency. This paper proposed a new hybrid ε-lexicography-weighted-sum that eliminates the need to normalize or scalarize objectives. The first layer of the model schedules MMG resources with DRP to minimize operating costs (local generation and power transactions with the utility grid) and maximize MMG profit. The second layer achieves the environmental operation of the MMG, while the third layer maximizes MMG reliability. This paper also proposed a new application of a recently developed enhanced equilibrium optimizer (EEO) for solving the three-layer EM problem. In addition, the uncertainties of solar power generation, wind power generation, load demand, and energy prices are considered based on the probabilistic 2m + 1 Point estimation method (PEM) approach. Three case studies are presented to verify the proposed MLMO approach on an MMG test system. In Case I, a deterministic EM is solved to simulate the MMG as a single layer to minimize costs and maximize benefits through DRP, while Case II solves the MLMO optimization problem. Simulation results show that the proposed MLMO technique reduces environmental emissions by 2.45% and 3.5% in its optimization layer and at the final layer, respectively. The independence index is also enhanced by 2.49% and 4.8% in its layer only and as a total increase, respectively. Case III is for the probabilistic EM simulation; due to the uncertain variables effect, the mean value in this case is increased by about 2.6% over Case I., (© 2024. The Author(s).)

Background: Patients with Polycystic ovary syndrome (PCOS) experienced endocrine disorders that may present vascular function changes. This study aimed to classify and predict PCOS by radial pulse wave parameters using machine learning (ML) methods and to provide evidence for objectifying pulse diagnosis in traditional Chinese medicine (TCM). Methods: A case-control study with 459 subjects divided into a PCOS group and a healthy (non-PCOS) group. The pulse wave parameters were measured and analyzed between the two groups. Seven supervised ML classification models were applied, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees, Random Forest, Logistic Regression, Voting, and Long Short Term Memory networks (LSTM). Parameters that were significantly different were selected as input features and stratified k-fold cross-validations training was applied to the models. Results: There were 316 subjects in the PCOS group and 143 subjects in the healthy group. Compared to the healthy group, the pulse wave parameters h3/h1 and w/t from both left and right sides were increased while h4, t4, t, As, h4/h1 from both sides and right t1 were decreased in the PCOS group (P < 0.01). Among the ML models evaluated, both the Voting and LSTM with ensemble learning capabilities, demonstrated competitive performance. These models achieved the highest results across all evaluation metrics. Specifically, they both attained a testing accuracy of 72.174% and an F1 score of 0.818, their respective AUC values were 0.715 for the Voting and 0.722 for the LSTM. Conclusion: Radial pulse wave signal could identify most PCOS patients accurately (with a good F1 score) and is valuable for early detection and monitoring of PCOS with acceptable overall accuracy. This technique can stimulate the development of individualized PCOS risk assessment using mobile detection technology, furthermore, gives physicians an intuitive understanding of the objective pulse diagnosis of TCM. Trial registration: Not applicable. [ABSTRACT FROM AUTHOR]

Traditional Chinese medicine (TCM) is an experienced‐based discipline and plays an important role in the current medical system. Digitalizing key features is the best way to con‐duct an in‐depth analysis of TCM. However, due to its complex generation mechanism, the crucial pulse‐taking procedure is hard to be digitalized and analyzed. This article fabricated a flexible piezoelectric sensor to gather data for the pulse‐taking procedure. The sensitivity of the proposed flexible sensor reached 0.34 V/kPa, so we adopted peripheral circuits to make its output signals stay in a reasonable range (± 0.5 V) for convenient data collection. We used the sensor to recognize whether a woman is pregnant since it has solid golden standards. The pulse signal was fused via mapping the signal spectrogram into a 3‐dimension tensor. The fused matrix was then processed by GoogleNet and reached a 90.48% correction rate. This result shows that the pulse condition has statistical differences, which solidified the objectivity of pulse diagnosis and escalated the TCM's digitalization level. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Education Research (1680-6360) is the property of Angle Publishing Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Age-related declines in adult affective theory of mind (AToM) have been discovered. However, AToM measures have not accounted for emotional state complexity involved in AToM. Measures have also not accounted for different types of relationships – friends versus strangers – for which AToM is employed, which is important considering the limited social networks of aging adults. We address these issues and examine the emotion complexity, social-relatedness, and contextual relevance in AToM across adult ages (18–89 years) using a new task and two well-established measures. The new task displayed good structural fit and internal construct validity. Overall, an age-related decline in AToM was found along with an interaction between age and emotion complexity. For all ages, AToM performance was best for complex emotions. However, as age increased, there was more rapid decline in AToM for more complex emotions than for less complex ones. Surprisingly, AToM performance for strangers was better than for social companions. The findings suggest age-related AToM declines are more nuanced than previously understood given that adult age differences are related to emotional state complexity. They indicate that the emotion complexity levels of basic, complex, and self-conscious should be included in AToM assessments. Implications for AToM tasks and development are discussed. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources (RESs) has become more attractive to provide electricity to rural and remote areas, which increases the reliability and sustainability of the electrical system, particularly for areas where electricity extension is difficult. Despite this, the integration of hybrid RESs is accompanied by many problems as a result of the intermittent and unstable nature of RESs. The extant literature has discussed the integration of RESs, but it is not comprehensive enough to clarify all the factors that affect the integration of RESs. In this paper, a comprehensive review is made of the integration of RESs. This review includes various combinations of integrated systems, integration schemes, integration requirements, microgrid communication challenges, as well as artificial intelligence used in the integration. In addition, the review comprehensively presents the potential challenges arising from integrating renewable resources with the grid and the control strategies used. The classifications developed in this review facilitate the integration improvement process. This paper also discusses the various optimization techniques used to reduce the total cost of integrated energy sources. In addition, it examines the use of up-to-date methods to improve the performance of the electrical grid. A case study is conducted to analyze the impact of using artificial intelligence when integrating RESs. The results of the case study prove that the use of artificial intelligence helps to improve the accuracy of operation to provide effective and accurate prediction control of the integrated system. Various optimization techniques are combined with ANN to select the best hybrid model. PSO has the fast convergence rate for reaching to the minimum errors as the Normalized Mean Square Error (NMSE) percentage reaches 1.10% in 3367.50 s. [ABSTRACT FROM AUTHOR]

The deployment of distributed and affordable renewable energy has led to the development of the prosumer concept in the field of energy. To better understand its relevance and to analyse the main trends and research developments, a systematic literature review was performed. This work gathered 1673 articles related to this topic that were analysed following the PRISMA methodology with the help of VOSviewer 1.6.18 bibliometric software. These papers are classified into four clusters: smart grids, microgrids, peer to peer networks, and prosumers. The first two clusters show a certain degree of maturity, while the latter maintain a growing interest. The analysis of the articles provides a broad view of the prosumer's role in energy and its potential, which is not limited to simple energy exchanges. Furthermore, this systematic review highlights the challenges, not only technical but also in terms of electricity market design and social aspects. The latter require further research, as society is undergoing a paradigm shift in the way in which energy is produced and used. How this shift occurred will determine whether it can lead to true prosumer empowerment and a fairer energy transition. [ABSTRACT FROM AUTHOR]

Copyright of Karaelmas Science & Engineering Journal / Karaelmas Fen ve Mühendislik Dergisi is the property of Karaelmas Science & Engineering Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recent research and developments in cloud robotics (CR) require appropriate knowledge representation to ensure interoperable data, information, and knowledge sharing within cloud infrastructures. As an important branch of the Internet of Things (IoT), these demands to advance it forward motivates academic and industrial sectors to invest on it. The IEEE 'Ontologies for Robotics and Automation' Working Group (ORA WG) has been developing standard ontologies for different robotic domains, including industrial and autonomous robots. The use of such robotic standards has the potential to benefit the Cloud Robotic Community (CRC) as well, supporting the provision of ubiquitous intelligent services by the CR-based systems. This paper explores this potential by developing an ontological approach for effective information sharing in cloud robotics scenarios. It presents an extension to the existing ontological standards to cater for the CR domain. The use of the new ontological elements is illustrated through its use in a couple of CR case studies. To the best of our knowledge, this is the first work ever that implements an ontology comprising concepts and axioms applicable to the CR domain. [ABSTRACT FROM AUTHOR]

Recent advances in flexible pressure sensors have fueled increasing attention as promising technologies with which to realize human epidermal pulse wave monitoring for the early diagnosis and prevention of cardiovascular diseases. However, strict requirements of a single sensor on the arterial position make it difficult to meet the practical application scenarios. Herein, based on three single-electrode sensors with small area, a 3 × 1 flexible pressure sensor array was developed to enable measurement of epidermal pulse waves at different local positions of radial artery. The designed single sensor holds an area of 6 × 6 mm2, which mainly consists of frosted microstructured Ecoflex film and thermoplastic polyurethane (TPU) nanofibers. The Ecoflex film was formed by spinning Ecoflex solution onto a sandpaper surface. Micropatterned TPU nanofibers were prepared on a fluorinated ethylene propylene (FEP) film surface using the electrospinning method. The combination of frosted microstructure and nanofibers provides an increase in the contact separation of the tribopair, which is of great benefit for improving sensor performance. Due to this structure design, the single small-area sensor was characterized by pressure sensitivity of 0.14 V/kPa, a response time of 22 ms, a wide frequency band ranging from 1 to 23 Hz, and stability up to 7000 cycles. Given this output performance, the fabricated sensor can detect subtle physiological signals (e.g., respiration, ballistocardiogram, and heartbeat) and body movement. More importantly, the sensor can be utilized in capturing human epidermal pulse waves with rich details, and the consistency of each cycle in the same measurement is as high as 0.9987. The 3 × 1 flexible sensor array is employed to acquire pulse waves at different local positions of the radial artery. In addition, the time domain parameters including pulse wave transmission time (PTT) and pulse wave velocity (PWV) can be obtained successfully, which holds promising potential in pulse-based cardiovascular system status monitoring. [ABSTRACT FROM AUTHOR]

Demand‐Side Management (DSM) Strategic plan of International Energy Agency states that the first option in all energy policies to achieve sustainable, reliable, and economic systems is DSM activities such as demand response programs (DRP). Thereby, these programs must also be considered in energy management of microgrids (MGs) and their operation and resiliency optimization. Energy management in MGs is carried out with different objectives such as reducing operation costs and enhancing the resilience response. In the first approach, the microgrid operator attempts to minimize the energy cost supplied by available resources in normal conditions. On the other hand, the goal of improving microgrid resilience response is to reduce the energy outages and load interruptions in emergencies such as occurrence of floods, earthquakes, and hurricanes. Naturally, in the second approach, the generation share of the microgrid resources itself in supplying the required demand is more; because the microgrid and main network connection will be lost in this approach. Since these two approaches may lead to different strategies in microgrid energy management, a compromise must be reached between them. In this paper, DRP, storage devices, renewable resources, such as wind and photovoltaic (PV) units, are modelled to manage energy in the MGs equipped with Combined Cooling, Heat, and Power to minimize the operation costs and enhance the resilience response. Three case studies are considered. The first case deals with the management of energy and structure in MGs without considering resilience objective function and responsive loads. In the second case, the resilience objective function is considered in the problem, and in the third study, responsive loads were also taken into account. The results show that the total objective function is reduced by considering the resilience objective function in case two. Also, the use of responsive loads in the third case also reduces more the total costs of MGs. [ABSTRACT FROM AUTHOR]

Pneumoconiosis has been the most harmful occupational disease for coal miners for decades and is difficult to cure completely. If this disease can be diagnosed and cured in time, it can be effectively controlled. Currently, the most reliable clinical diagnosis method is using X‐ray or high‐resolution computerized tomography (CT). Here, we propose a method to diagnose pneumoconiosis using wrist pulse signals. First, a pulse sensor is used to collect wrist pulse signals, then, preprocessing is conducted, and a single period of a pulse signal is separated. Second, a 13‐dimensional feature is extracted in the time, frequency, and wavelet domains. Finally, a support vector machine, back propagation neural network, or random forest classifier are used to classify the pulse signals, two voting procedures are used and the diagnosis result can be achieved. The lowest recognition accuracy is 85.16%. The experimental results show that our 13‐dimensional feature can be used as the main feature of pneumoconiosis diagnosis. The proposed method provides a more reliable auxiliary means for the diagnosis of coal mine pneumoconiosis. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Freezing of gait (FOG), one of the most disabling features of Parkinson's disease (PD), is a brief episodic absence or marked reduction in stride progression despite the intention to walk. Progressively more people who experience FOG restrict their walking and reduce their level of physical activity. The purpose of this study is to develop and validate a physical mobility task that induces freezing of gait in a controlled environment, employing known triggers of FOG episodes according to the literature. To validate the physical mobility tasks, we recruited 10 volunteers that suffered PD-associated freezing (60.6 ± 7.29 years-old) with new FOG-Q ranging from 12 to 26. The validation of the proposed method was carried out using inertial sensors and video recordings. All subjects were assessed during the OFF and ON medication states. The total number of FOG occurrences during data collection was 144. The proposed tasks were able to trigger 120 FOG episodes, while the TUG test caused 24. The Inertial Measurement Unit (IMU) with accelerometer and gyroscope could not only detect FOG episodes but also allowed us to visualize the three types of FOG: akinesia, festination and trembling in place. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) have been employed in various real-time applications and addressed fundamental issues, such as limited power resources and network life. Several sensor nodes in a WSN monitor the actual world and relay discovered data to base stations. The biggest issue with WSN is that the sensors have a limited lifetime and use much electricity to relay data to the base station. This paper proposes an improved PSO-based Enhanced Distributed Energy Efficient Clustering (EDEEC) algorithm to extend the network's life and reduce power consumption. Clustering is the process of forming groups of sensor nodes. The cluster aims to improve the network's scalability, energy efficiency, and other characteristics. The particle swarm optimization algorithm is modified to obtain energyefficient WSNs. The assessment is based on the essential WSN characteristics, including network lifetime and energy efficiency (power consumption). Compared to LEACH, HEED, and DEEC, our proposed IPSO-EDEEC uses less energy. [ABSTRACT FROM AUTHOR]

Rigid robotic grippers are not capable of self-adjusting their grip size based on any real-time changes in the dimensions of the target object. This problem of self-adjustment can be addressed by soft robotic grippers. In the past few decades, new methods, and techniques to develop and control soft actuators have been explored by researchers worldwide. Soft robotic grippers can be categorized into three technologies which are controlled actuation, controlled adhesion, and controlled stiffness. Using these three technologies soft robots can mimic the morphology of the gripping and locomotion mechanisms of various animals. This study is aimed to present an introductory review for researchers who want to explore the field of soft robotics. While previous reports focussed on granular jamming structures to control stiffness, the present study emphasized laminar jamming structures and discussed the recent soft actuators developed using these structures. It was observed that soft actuators with a longitudinal strain like mckibben and peano HASEL exert high forces compared to bending actuators. Also, sandwiched laminar jammers can generate greater stiffness compared to homogeneous laminar jammers. [ABSTRACT FROM AUTHOR]

Summary: This article presents an innovative perspective of using a gravity energy storage system namely gravity power module (GPM) around a multi‐storied building. Two number of GPMs have been vertically fixed around the walls of a multi‐storied building and have not been placed underground following the traditional way. The grid‐connected proposed hybrid system consisting of solar PV, GPMs and vanadium redox flow battery is focused to supply the maximum possible amount of an essential load of a high‐riser, through renewable energy. All of the storage systems are charged through solar PV. Whereas, the grid is used, to procure energy for the unmet load demand and sell the excess renewable energy. Results simulated for each month following seasonal load variation show that net energy supplied by the hybrid system is 99% of the full load demand of essential equipment of the building for the month of February and 90% to 91% for the month of January, November and December. During all the months, the supply of gravity energy storage is restricted to supply within the 18th to 24th hour to reduce the peak demand of the grid. The gravity energy storage is also considered as the primary storage system during those intervals. The yearly average of energy supply from the renewable resource in the proposed system toward the building load is found to be 47.77%. While considering the renewable energy sale toward the grid along the load supply of the building the yearly average of renewable energy penetration becomes 48.89%. [ABSTRACT FROM AUTHOR]

The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors. [ABSTRACT FROM AUTHOR]

Wearable pulse detection devices can be used for daily human healthcare monitoring; however, the relatively poor flexibility and low sensitivity of the pulse detection devices are hindering the scrutiny of pulse information during pulse diagnosis of different pulse positions. This paper developed a novel and wearable pulse detection device based on three flexible pressure sensors using synthetic graphene and silver composites as the pressure sensing material. The structural design of the pulse detection device is firstly presented; the core component of pressure sensors is using the sawtooth protrusions to convert pressure induced by radial pulse vibrations into localized deformation of graphene composites. The fabricated pulse detection device is characterized by high pressure sensing performance, including relatively high sensitivity (8.65% kPa−1), broad sensing range (12 kPa), and good dynamic response with a response time of about 100 ms. Then, the pulse detection device is worn on a human wrist to detect the pulses from three pulse positions, namely, 'Cun', 'Guan', and 'Chi', and the results demonstrated the capability of using our device to detect pulse signals. The physical conditions of the subject, such as arterial stiffness index, can be further analyzed through the characteristics of the acquired pulse signals, demonstrating the potential application of using wearable pulse detection devices for human health monitoring. [ABSTRACT FROM AUTHOR]

This paper aims to minimize the generation cost of a low voltage (LV) grid-connected microgrid system using a novel hybrid whale optimization algorithm (WOA)- Sine cosine algorithm (SCA) optimization technique. Three different methods of electricity market prices are formulated in turns and the generation cost is evaluated for each method to sort out the best among them which yields the least generation cost of the same microgrid system. Both active and passive participation of grid are studied to acknowledge its importance. Numerical results show that the time of usage (TOU) method of electricity market pricing proved to be the most convenient and economic method for cost analysis of the system. The generation cost was at its maximum when the electricity price was a constant value. Also during the TOU method of electricity pricing, a 15% surge in the price was realized when the grid was passively participating in supplying power to the system. Figures and statistical data aids the fact that proposed hybrid WOASCA outperformed WOA in consistently providing a robust and superior quality result. [ABSTRACT FROM AUTHOR]

This study examined the potential effects of artificial intelligence on Turkish education. A qualitative research approach was employed by posing an open-ended question to academics in order to attain this objective thanks to built-in capabilities for conducting complicated computer operations, cloud-based services, and conciliatory accession for agile network connections. This study emphasizes that Turkey is highly fragmented and consists of various business organizations at both the municipal and regional levels. The two main policy documents produced by the Turkish government suggest that colleges play a strong role in national and regional Artificial Intelligence (AI) strategies for workforce growth, with substantial consequences for AI adoption strategies. These documents include information on three well-known educational entities: The new oriental workgroups, recurrent neural networks, and classroom clustering. Significant aspects of Turkey's educational AI growth include a strong private education industry and a growing international interest. The investigation results revealed a decline in the level of understanding regarding the methods of using artificial intelligence, indicating the necessity for additional awareness-raising in Turkey. [ABSTRACT FROM AUTHOR]

A novel solution for optimized energy management comprising a microgrid system for industries in Pakistan is proposed. The proposed study considered microgrids based on photovoltaics, wind turbines, power storage systems, and dual-fuel (DF) generators as backup. A heuristic methodology with a cuckoo search algorithm (CSA) is presented for efficient power trading by scheduling machines. The study was conducted to prove that CSA is adaptable and flexible for self-governing choices for the efficient management and scheduling of machines and power trade between the microgrid and commercial grid. A mixed integer linear programming algorithm is introduced to optimize the system design problems that control decision making for the ideal operation management. A real-time pricing scheme is utilized for electricity price figures. The simulation results show the efficient performance of the proposed scheme to maximize profitability, reduction in electricity cost, and peak to average ratio. Furthermore, the proposed optimization technique was compared with a highly in-use strawberry algorithm to prove the supremacy of the proposed technique. The proposed efficient and robust energy management system was implemented in Shafi Dyeing Industry, Faisalabad, to validate the simulated model. [ABSTRACT FROM AUTHOR]

Copyright of Health Problems of Civilization is the property of Termedia Publishing House and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Pixo: Revista de Arquitetura Cidade e Contemporaneidade is the property of Pixo Revista de Arquitetura Cidade e Contemporaneidade and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Parkinson Disease (PD) primarily affects older adults. It is the second-most common neurodegenerative disease after Alzheimer's disease. Currently, more than 10 million people suffer from PD, and this number is expected to grow, considering the increasing global longevity. Freezing of Gait (FoG) is a symptom present in approximately 80% of advanced-stage PD's patients. FoG episodes alter the continuity of gait, and may be the cause of falls that can lead to injuries and even death. The recent advances in the development of hardware and software systems for the monitoring, stimulus, or rehabilitation of patients with FoG has been of great interest to researchers because detection and minimization of the duration of FoG events is an important factor in improving the quality of life. This article presents a review of the research on non-invasive medical devices for FoG, focusing on the acquisition, processing, and stimulation approaches used. [ABSTRACT FROM AUTHOR]

Artificial intelligence is an emerging technology that revolutionizes human lives. Despite the fact that this technology is used in higher education, many professors are unaware of it. In this current scenario, there is a huge need to arise, implement information bridge technology, and enhance communication in the classroom. Through this paper, the authors try to predict the future of higher education with the help of artificial intelligence. This research article throws light on the current education system the problems faced by the subject faculties, students, changing government rules, and regulations in the educational sector. Various arguments and challenges on the implementation of artificial intelligence are prevailing in the educational sector. In this concern, we have built a use case model by using a student assessment data of our students and then built a synthesized using generative adversarial network (GAN). The dataset analyzed, visualized, and fed to different machine learning algorithms such as logistic Regression (LR), linear discriminant analysis (LDA), K-nearest neighbors (KNN), classification and regression trees (CART), naive Bayes (NB), support vector machines (SVM), and finally random forest (RF) algorithm and achieved a maximum accuracy of 58%. This article aims to bridge the gap between human lecturers and the machine. We are also concerned about the psychological emotions of the faculty and the students when artificial intelligence takes control. [ABSTRACT FROM AUTHOR]

With the rapid development and application of artificial intelligence (AI) in medical field, the diagnostic ways of human health and the social medical structures have changed. Based on the concept of holism and the theory of syndrome differentiation and treatment, TCM realizes comprehensive informatization and intelligence with the help of AI technology in data mining, intelligent diagnosis and treatment, intelligent learning, and decision-making. Furthermore, the intelligent research of TCM technology will further promote the improvement in TCM diagnosis and treatment rules and the leaping development of TCM intelligent instruments. In this article, we performed a systematic review of scientific literature about TCM and AI. Moreover, the practical problems of TCM intellectualization, the current situation and demand of TCM, and the influence of AI in the TCM field are discussed by searching for literature using TCM scientific databases, reference lists, expert consultation, and targeted websites. Finally, we look forward to the application prospects of AI and propose a possible future direction of intelligent TCM in the current health-care system in China. [ABSTRACT FROM AUTHOR]

The optimization of planning and operation for distributed energy systems (DESs) is often converted into an approximated mixed-integer linear programming (MILP) problem. To improve the MILP model's accuracy, it is necessary to linearize many non-linear factors, and one of the most critical factors is off-design efficiency. In this study, the off-design performance of common energy conversion technologies and their effect on optimization reliability are investigated through a literature review and dynamic simulation models. It is found that most technologies can maintain high and stable efficiency within a certain range of load, and the off-design characteristics of certain technologies can be ignored. Therefore, in our MILP model, the off-design efficiency is simplified by optimizing the number of certain types of devices while simultaneously limiting their load to a certain range and ignoring the off-design characteristics of other types of devices. This method allows the devices to always work with high efficiency, due to perfect working conditions. The optimization results prove that compared to the method considering the off-design efficiency of all technologies, the maximum deviation is only 2.29%, demonstrating enough accuracy of the proposed method. Owing to the simplification of off-design efficiency, more non-linear factors are promising to add in the model. [ABSTRACT FROM AUTHOR]

A new generation of systems appeared with the birth of Cyber-Physical Systems (CPS) that integrated computational and physical capabilities. CPS represents an emerging domain as it has an important interest in the literature due to its interaction with Structural Health Monitoring System applications. In this paper, we present a deep investigation of SHM and wireless sensing technologies towards an efficient CPS. Structural Health Monitoring (SHM) based on wireless sensor networks (WSNs) has the potency to reduce the cost of installation and maintenance of public and private infrastructure. Many types of research are interested in SHM using WSN due to its application domain diversity and its importance in public safety. WSN networks can be a prominent candidate to solve many SHM problems thanks to its implementation simplicity and its significant cost reductions. We present a comprehensive survey about SHM based on new technologies and methods including the internet of things (IoT), Software-defined Networking (SDN), fog and cloud computing. SHM application domains are also highlighted with analytic research domains, projects, testbeds, and experimental works. Besides that, this investigation pinpoints SHM functionalities (damage detection, prognostic and risk assessment) and Artificial Intelligence (AI) contributions for SHM such as sensor placement and clustering and its benefits on energy optimization. The main challenges of WSN design, energy consumption, damage prediction, SHM mobility, SHM large scale were presented and discussed. [ABSTRACT FROM AUTHOR]

This study proposes a novel strategy to utilize photovoltaic (PV) system energy coupled with grid connection and dual battery storage system. First, the problem is formulated in mathematical form with a linear model approach; later, a cost function is defined to evaluate the tariff in different scenarios. The cost function is designed to minimize the burden on consumers' shoulders in a constrained environment. The battery storage systems are used intelligently to reduce imported and exported energy, hence the respective cost. The secondary battery system has been added and discharges during high tariff hours provided the state of charge of the primary battery system is at the minimum level. Various types of tariff incentives are considered for comprehensive analysis. Various cases are considered for the analysis: grid supply, PV-system alone, grid connection coupled with PV-system, and grid connection connected with PV-system and battery storage banks. The statistical analysis is performed in each case to have a detailed observation on resultant distributions of imported energy, exported power, and stored energy based on unmet and excess power. The results conclude that the proposed methodology reduces the imported and exported power requirement with a net profit of £ 46. [ABSTRACT FROM AUTHOR]

Introduction: Human adaptation to high altitude is due to characteristic adjustments at every physiological level. Differences in lipid profile and cardiovascular risk factors in altitude dwellers have been previously explored. Nevertheless, there are no reports available on genotype-controlled matches among different altitude-adapted indigenous populations. Objective: To explore the possible differences in plasma lipid profile and cardiovascular risk among autochthonous Kiwcha people inhabitants of low and high-altitude locations. Methodology: A cross-sectional analysis of plasmatic lipid profiles and cardiovascular risk factors in lowland Kiwchas from Limoncocha (230 m) and high-altitude Kiwchas from Oyacachi (3,800 m). Results: In the low altitude group, 66% were women (n = 78) and 34% (n = 40) were men, whereas in the high altitude group, 59% (n = 56) were women and 41% (n = 41%) were men. We found the proportion of overweight and obese individuals to be higher among low altitude dwellers (p < 0.05). Red blood cells (RBCs), hemoglobin concentration, and SpO2% were higher among high altitude dwellers and the erythrocyte size was found to be smaller at high altitude. The group located at low altitude also showed lower levels of plasma cholesterol, low-density lipoprotein (LDL), and high-density lipoprotein (HDL), but most of these differences are not influenced by gender or elevation. Conclusions: Living at an altitude elicits well-known adaptive physiological changes such as erythrocyte count, hemoglobin concentration, hematocrit level, and serum glucose level. We also report clinical differences in the plasma lipid profile, with higher levels of cholesterol, HDL, and LDL in inhabitants of the Andes Mountain vs. their Amazonian basin peers. Despite this, we did not find significant differences in cardiovascular risk. [ABSTRACT FROM AUTHOR]